Insta Sentiment
A customer intelligence platform for a payments app. AI agents read C-SAT surveys, ITSM tickets and WhatsApp chats in Arabic and English, score sentiment, flag likely fraud and trace spikes to their causes.
- Sentiment analysis
- Fraud signals
- Root-cause correlation
- AI agents
01
Overview
Insta Sentiment brings three customer signals into one place: C-SAT survey comments from the bot, IVR and WhatsApp channels, ITSM tickets from the call centre and incident sheets, and full WhatsApp chat transcripts. Uploads are checked against templates, then a supervisor agent routes them to specialist agents for chats, tickets, C-SAT and fraud.
- Category
- Customer intelligence
- Industries
- Payments & fintech · Banking & finance · Contact centres
02 · Business value
What it changes for the business
Insta Sentiment
See what customers are unhappy about, and why, across every channel, while there is still time to act.
- 01
Earlier incident response
Linking chat and ticket surges to open alerts for each bank points teams at a broken flow while it is still happening.
- 02
Fraud surfaced sooner
Likely fraud reports are pulled out of normal support traffic and grouped, so fraud teams can see patterns and act on them.
- 03
Less manual review
AI agents read every chat, ticket and survey comment, so analysts start from findings instead of raw text.
- 04
AI cost under control
Per-agent cost tracking and the option to switch agents on or off keep model spend visible and managed.
03 · The Problem → The Outcome
Challenges → Solutions
- 01
Challenges: Customer feedback is split across surveys, tickets and WhatsApp
Solutions: One dashboard with cross-channel KPIs and trends for C-SAT, ITSM and WhatsApp
- 02
Challenges: Nobody can read every Arabic and English chat and ticket by hand
Solutions: Specialist agents score every chat and ticket with a rationale and quoted evidence
- 03
Challenges: Fraud complaints are buried in ordinary support traffic
Solutions: A fraud agent tags likely fraud with method, risk level and evidence, and a fraud dashboard groups the cases
- 04
Challenges: When complaints spike, the cause is found too late
Solutions: The spike analyzer flags unusual hours and names the likely cause, with the evidence behind it
- 05
Challenges: Management reports are assembled by hand from several sources
Solutions: Reports by role, source and date range are generated in one click and saved
04 · Use cases
Where it fits, by industry
Payments & fintech
- 01
Cross-channel customer sentiment
A payments app sees how customers feel across surveys, tickets and WhatsApp, with the root cause of each complaint in plain words.
Banking & finance
Compliant AI solutions for banks and financial institutions.
- 01
Partner-bank incident correlation
When calls or chats about one bank jump while that bank has an open incident, the team is alerted and can tell the bank sooner.
- 02
Fraud signal detection
Complaints that point to fraud are tagged with method, amount and risk level and grouped across batches for the fraud team.
Contact centres
AI that reviews every conversation and helps contact-centre teams act on it.
- 01
Shift and daily reports
Call-centre leads get AI briefs for each batch and a consolidated daily report, written for their role.
05
Enterprise Capabilities
Intended Users
CX, call-centre and fraud teams, and the managers who read the shift and daily reports.
Insta Sentiment
Customer intelligence
- 01Template-checked uploads for C-SAT, ITSM and WhatsApp
- 02Supervisor and specialist AI agents
- 03Sentiment with root cause, evidence and next best actions
- 04Fraud tagging and a fraud dashboard
- 05Spike detection with cause analysis
- 06Live monitoring of incidents, surges and SLA breaches
06 · Highlights
Key Proof Point
Insta Sentiment
Every sentiment and fraud finding shows the customer's own words as evidence, and a supervisor agent samples results for quality before they reach a report.
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